Earliest accumulation of β-amyloid occurs within the default-mode network and concurrently affects brain connectivity — TEI-tagged view

Sebastian PalmqvistpersName, Michael SchöllpersName, Olof StrandbergpersName, Niklas MattssonpersName, Erik StomrudpersName, Henrik ZetterbergpersName, Kaj BlennowpersName, Susan LandaupersName, William JagustpersName, Oskar HanssonpersName (2017date). “Earliest accumulation of β-amyloid occurs within the default-mode network and concurrently affects brain connectivity.” Nature Communicationstitle·j.

Object of study — where amyloid-beta fibrils first accumulate in preclinical Alzheimer's diseaseterm

Research question — where Abeta fibrils begin to accumulate and whether this affects connectivity

Contribution — early Abeta accumulation localised to core default mode networkterm·dom regions

Full text — tagged

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Earliest accumulation of β-amyloid occurs within the default-mode network and concurrently affects brain connectivity Abstract It is not known exactly where amyloid-βterm·key (Aβterm) fibrils begin to accumulate in individuals with Alzheimer’s diseaseterm (ADterm). Recently, we showed that abnormal levels of Aβ42 in cerebrospinal fluidterm·key (CSF) can be detected before abnormal amyloid can be detected using PETterm·key in individuals with preclinical ADterm·key. Using these approaches, here we identify the earliest preclinical ADterm·key stage in subjects from the ADNI and BioFINDER cohorts. We show that Aβterm accumulation preferentially starts in the precuneus, medial orbitofrontal, and posterior cingulate cortices, i.e., several of the core regions of the default mode networkterm·dom (DMNterm·dom). This early pattern of Aβterm accumulation is already evident in individuals with normal Aβ42 in the CSF and normal amyloid PETterm·key who subsequently convert to having abnormal CSF Aβ42term·var. The earliest Aβterm accumulation is further associated with hypoconnectivityterm·dom within the DMNterm·dom and between the DMNterm·dom and the frontoparietal networkterm·dom, but not with brain atrophy or glucose hypometabolism. Our results suggest that Aβterm fibrils start to accumulate predominantly within certain parts of the DMNterm·dom in preclinical ADterm·key and already then affect brain connectivity.

Introduction Accumulation of aggregated amyloid-βterm·key (Aβterm) in the brain is believed to be the first pathological mechanism of Alzheimer’s diseaseterm (ADterm) 1 . Previous studies have shown that cerebral deposition of Aβterm fibrils can occur for decades before an individual with ADterm reaches the dementia stage 2 . The widespread cerebral distribution of Aβterm in later ADterm stages is well-established 3 , 4 , however it has been difficult to accurately identify the initial brain regions of Aβterm accumulation due to the long time-lag between the start of the pathophysiology and onset of symptoms.

A neuropathological staging of different Aβterm phases in ADterm has been proposed based on post-mortem examinations and it has been shown that the first phase is characterized by Aβterm deposits throughout the frontal, parietal, temporal, and occipital neocortex 5 . Studies employing positron emission tomography (PETterm·key) to visualize fibrillar Aβterm deposits have also attempted to identify early Aβ-accumulating regions. For example, when analyzing the earliest data from ADterm mutation-carriers, most cortical areas except for the sensorimotor cortex showed Aβterm accumulation as early as 15 years before the expected onset of symptoms 6 . A shortcoming of such studies is the cross-sectional design, which precludes conclusions about longitudinal events. Another issue is that it has previously been difficult to identify the earliest preclinical ADterm·key cases; when mixing such cases with later asymptomatic ADterm cases that have been accumulating Aβterm fibrils for years, the cortical sites of accumulation of Aβterm identified are widespread.

In the present study, we applied a novel methodology of combining data obtained regarding Aβterm levels in the brain via PETterm·key, and levels of the Aβ42 peptide in cerebrospinal fluidterm·key (CSF), to identify individuals who have just recently started to accumulate Aβterm in the brain. Previous cross-sectional studies indicate that Aβ42 levels decrease in the CSF as Aβterm starts to accumulate, before fibrillar plaques can be detected with PETterm·key 7 – 11 . Recently, this was confirmed longitudinally in a cohort of more than 400 non-demented individuals where we found that those with abnormally low CSF levels of Aβ42, but with still normal Aβterm levels detected by PETterm·key (CSF+/PETterm·key−), accumulated Aβterm fibrils longitudinally at a similar rate as those who had both abnormal CSF levels of Aβ42 and abnormal levels of Aβterm detected by PETterm·key (CSF+/PETterm·key+) and four times higher than those with normal biomarker results on both parameters (CSF−/PETterm·key−) 12 . This indicates that individuals with ADterm may first be defined as being CSF+/PETterm·key−, and then progress to CSF+/PETterm·key+. It might therefore be possible to identify the earliest stages of preclinical ADterm·key by studying individuals with abnormal CSF Aβ42term·var and normal Aβterm PETterm·key (CSF+/PETterm·key−, also referred to as early Aβ accumulatorsterm·dom to indicate their stage in the accumulation of Aβterm).

The main hypothesis in the present study was that Aβterm fibrils are more prone to start aggregating in certain regions of the brain before they can be found throughout the neocortex, and before neurodegenerationterm·dom is present. Identifying these regions and examining their properties can provide important information about why Aβterm aggregation starts in sporadic ADterm, which is a crucial step for developing disease-modifying drugs. The aim of the study was therefore to identify such regions by comparing the longitudinal fibrillar Aβterm deposition rates in different brain regions of early Aβ accumulatorsterm·dom (termed CSF+/PETterm·key−) with non-accumulators (termed CSF−/PETterm·key−). To avoid bias from non-optimal cut-offs for CSF Aβ42term·var levels, we also examined the correlation between continuous CSF Aβ42term·var levels and the regional Aβterm PETterm·key signal in PETterm·key negative subjects. We performed the analyses in 68 predefined anatomical regions as well as employing voxelwise statistics. The analyses were performed both in a main cohort and an independent validation cohort. As the main cohort, we used the North American Alzheimer’s Diseaseterm Neuroimaging Initiative (ADNI) and included all non-demented participants with CSF Aβ42term·var and longitudinal Aβterm PETterm·key data (473 individuals). The Swedish BioFINDER study was used as the validation cohort, from which we included all non-demented participants with baseline CSF Aβ42term·var and Aβterm PETterm·key data (406 individuals).

We also examined early regional Aβterm accumulation in the presumably earliest Aβterm accumulators; those with normal CSF Aβ42term·var and normal Aβterm PETterm·key at baseline who converted to abnormal CSF Aβ42term·var levels at follow-up but still had a normal Aβterm levels identified by PETterm·key scan (CSF−/PETterm·key to CSF+/PETterm·key−). Consistently, using different cohorts and methods, we identified a specific set of early Aβ-accumulating regions, which largely corresponded to a functional brain network, the default mode networkterm·dom (DMNterm·dom). The relationship between early stage Aβterm accumulation and whole-brain functional connectivityterm·key was examined in detail and revealed that hypoconnectivityterm·dom, especially within the DMNterm·dom and between DMNterm·dom and the frontoparietal networkterm·dom, was associated with low CSF Aβ42term·var levels in individuals that still had normal Aβterm PETterm·key scans.

To test the hypothesis that early Aβterm accumulation is an upstream mechanism that precedes overtly decreased energy metabolism and cell death, we compared the longitudinal changes in 18 F-fluorodeoxyglucose (FDG) PETterm·key and volumetric magnetic resonance imaging (MRI) in the different groups. Finally, we examined the validity of using CSF/PETterm·key groups to stratify Aβterm stages by comparing early (CSF+/PETterm·key−) and late (CSF+/PETterm·key+) accumulators to identify known late regions of Aβterm accumulation, including the sensorimotor cortex.

Results Experimental outline and baseline characteristics We first analyzed the longitudinal results from the ADNI cohort, then replicated the result in the BioFINDER cohort and finally analyzed functional connectivityterm·key in BioFINDER.

Baseline characteristics of the ADNI sample and the different CSF/PETterm·key groups are provided in Table 1 . Early stage Aβterm accumulators (CSF+/PETterm·key−) had significantly higher prevalence of the APOE ε4 genotype and a slightly higher neocortical florbetapir standardized uptake value ratio (SUVR), but similar baseline cognition, hippocampal volume, and CSF tau levels as non-accumulators (CSF−/PETterm·key−) (Table 1 ). By contrast, the non-demented late stage Aβterm accumulators (CSF+/PETterm·key+) showed, at baseline, signs of neurodegenerationterm·dom with hippocampal atrophy, higher CSF tau levels and worse cognition, and had per definition significantly increased florbetapir SUVRs (Table 1 ). Table 1 Baseline characteristics of the ADNI cohort A B C p - value Entire ADNI sample CSF−/PETterm·key− CSF+/PETterm·key− CSF+/PETterm·key+ N 473 218 59 191 Baseline CN/MCI 37%/63% 47%/53% 44%/56% 23%/77% A-B = 0.58 B - C < 0.001 A - C < 0.001 Age (years) 72.0 (7.0) 71.0 (7.0) 71.2 (8.1) 73.2 (6.3) A-B = 0.99 B-C = 0.20 A - C < 0.001 Gender (women) 47% 47% 46% 48% 0.95 Education (years) 16.5 (2.6) 16.7 (2.6) 17.0 (2.2) 16.1 (2.6) A-B = 0.47 B - C = 0.007 A - C = 0.03 Presence of the APOE ε4 allele 40% 16% 46% 65% A - B < 0.001 B - C = 0.004 A - C < 0.001 MMSE (0–30 points) 28.4 (1.7) 28.8 (1.4) 28.8 (1.3) 27.9 (1.8) A-B = 0.95 B - C = 0.001 A - C < 0.001 Baseline neocortical composite florbetapir SUVR 1 0.90 (0.21) 0.74 (0.05) 0.78 (0.06) 0.93 (0.09) A - B < 0.001 B - C < 0.001 A - C < 0.001 Years between florbetapir PETterm·key scans (range) 2.0 (0.9–4.1) 2.0 (0.9–4.1) 2.0 (1.9–4.1) 2.0 (1.7–3.0) 1.0 Years between FDG PETterm·key scans (range) 2.0 (0.8–3.1) 2.0 (0.8–2.7) 2.1 (1.9–3.1) 2.0 (1.8–3.0) 0.72 Years between MRI scans (range) 2.0 (0.4–4.1) 2.0 (0.4–4.1) 2.0 (0.5–4.1) 2.0 (0.6–3.1) 0.92 Hippocampus volume (cm 3 ) 7.2 (1.1) 7.5 (1.0) 7.5 (0.9) 6.9 (1.3) A-B = 0.92 B - C < 0.001 A - C < 0.001 CSF Aβ42term·var (ng/L) 186 (52) 234 (27) 165 (23) 136 (22) A - B < 0.001 B - C < 0.001 A - C < 0.001 CSF T-tau (ng/L) 79 (46) 59 (26) 60 (31) 109 (53) A-B = 0.95 B - C < 0.001 A - C < 0.001 CSF P-tau (ng/L) 39 (23) 28 (12) 30 (26) 54 (25) A-B = 0.19 B - C < 0.001 A - C < 0.001 CN cognitively normal, CSF cerebrospinal fluidterm·key, CSF+ /− abnormal/normal CSF Aβ42term·var levels, MCI mild cognitive impairment, MMSE Mini Mental State Examination, N number of subjects, PETterm·key+ /− abnormal/normal florbetapir SUVR, SD standard deviation, SUVR standardized uptake value ratio (using a composite reference region) corrected for partial volume errors. Comparison between non-accumulators (CSF−/PETterm·key−), early Aβ accumulatorsterm·dom (CSF+/PETterm·key−), and late Aβterm accumulators (CSF+/PETterm·key+). Values are given in mean (SD) if not otherwise specified. Groups were compared with Mann–Whitney U statistics if significant after the Kruskal–Wallis test. Significant p values are in bold. The CSF−/PETterm·key+group was not included in this table as a separate group since they were not used in any analysis and they were also too few to be used in the group comparisons ( n = 5) Identifying early Aβ-accumulating regions To identify early Aβterm accumulation regions, longitudinal voxelwise comparisons of accumulation rates (SUVR/year) were performed between CSF+/PETterm·key− (early Aβ accumulatorsterm·dom) and CSF−/PETterm·key− (non-accumulators). The results are shown in Fig. 1a (see Supplementary Fig. 1 for whole-brain axial images). The brain regions showing significant increases in Aβterm PETterm·key signal over time in CSF+/PETterm·key− cases included the medial orbitofrontal cortex, anterior cingulate cortex, posterior, and isthmus cingulate cortex, precuneus and to a lesser extent temporal regions. Similar significant regions were identified by correlating CSF Aβ42term·var levels with the accumulation rates in PETterm·key− subjects to remove potential biases from the Aβ42 cut-off (Fig. 1b ). In the region of interest (ROI)-based comparisons, 15 of 68 cortical regions had an increased rate in the CSF+/PETterm·key− subjects compared to the CSF−/PETterm·key− subjects (Table 2 and Supplementary Movie 1 ). The significant differences were in agreement with the voxelwise analyses and were mainly seen in the orbitofrontal cortex, precuneus, insula, and the posterior/isthmus cingulate cortex. On average the yearly Aβterm accumulation rate of the CSF+/PETterm·key− group was ~4–5 times that of the CSF−/PETterm·key− group in the significant brain regions (Table 2 ). All results were corrected for multiple comparisons and adjusted for gender, age, and time between PETterm·key scans. To account for atrophy as a confounding factor, we also adjusted all significant models for the annual change in cortical thickness of each ROI. After this adjustment, all 15 regions still differed significantly between the two groups. The 15 early Aβterm regions were also significant when adjusting for diagnosis (MCI or healthy control). Finally, we adjusted for APOE genotype to examine if different early Aβterm patterns were observed depending on whether one carried an APOE ε4 allele, but the same regions as in Table 2 were found (details shown in Supplementary Table 1 ). The distinct pattern of early Aβterm accumulation (Fig. 1a, b , Table 2 ) can be contrasted to the more global Aβterm accumulation pattern seen in late, but still non-demented, Aβterm accumulators (CSF+/PETterm·key+) compared with non-accumulators (CSF−/PETterm·key−) (Fig. 1c ). Fig. 1 Regions of Aβterm accumulation from longitudinal voxelwise analyses in ADNI. a shows the regions where Aβterm fibrils start to accumulate by comparing the annual florbetapir SUVR rate during 2 years between early stage Aβterm accumulators (CSF+/PETterm·key−, n = 59) and non-accumulators (CSF−/PETterm·key−, n = 218). The lateral and medial projections in a show that the most significantly increased accumulation rate among the early Aβ accumulatorsterm·dom was located in the posterior cingulate cortex, the precuneus, and the medial orbitofrontal cortex. A PET-data-derived ROI of these early-accumulating Aβterm regions is available at http://biofinder.se b confirms the regions in a without biases from a specific CSF Aβ42term·var cut-off. Here, we performed voxelwise correlations between annual florbetapir SUVR rates and CSF Aβ42term·var levels in Aβterm PETterm·key negative individuals ( n = 277). To contrast the early stage Aβterm regions, c shows the regions with significantly increased annual SUVR rate in late stage Aβterm accumulators (CSF+/PETterm·key+, n = 191) compared with non-accumulators (CSF−/PETterm·key−). A widespread pattern of Aβterm accumulation is seen in these non-demented CSF+/PETterm·key+ subjects. Voxelwise two-sample t -tests were used and all analyses in a – c are adjusted for age and gender. The significant threshold was set at p < 0.001. The red and yellow colors illustrate significant t values according to the scale on the left Table 2 Annual Aβterm accumulation rates in the significant early Aβterm regions in ADNI Region CSF+/PETterm·key− CSF−/PETterm·key− p -value Posterior cingulate cortex, right 2.7% (1.8–3.6) 0.9% (0.5–1.3) 0.000051 Medial orbitofrontal cortex, left 2.5% (1.4–3.6) 0.4% (−0.2–1.0) 0.00026 Medial orbitofrontal cortex, right 2.5% (1.3–3.7) 0.4% (−0.1–1.0) 0.00038 Precuneus, left 2.3% (1.4–3.3) 0.7% (0.2–1.2) 0.00051 Rostral anterior cingulate cortex, right 2.0% (1.3–2.8) 0.5% (0.0–1.0) 0.00081 Lateral orbitofrontal cortex, left 1.8% (0.9–2.7) 0.3% (−0.1–0.7) 0.0011 Insula, right 1.8% (0.8–2.8) 0.2% (−0.3–0.7) 0.0025 Isthmus cingulate cortex, left 2.0% (1.1–2.8) 0.5% (0.0–1.0) 0.0027 Rostral anterior cingulate cortex, left 1.9% (1.1–2.6) 0.6% (0.1–1.1) 0.0031 Isthmus cingulate cortex, right 2.1% (1.2–2.9) 0.5% (0.0–1.0) 0.0032 Precuneus, right 1.9% (1.1–2.8) 0.6% (0.2–1.1) 0.0041 Superior frontal cortex, left 1.2% (0.5–2.0) 0.2% (−0.2–0.5) 0.0053 Transverse temporal gyrus, right 0.8% (−0.3–1.9) −0.8% (−1.5–−0.2) 0.0074 Insula, left 1.5% (0.7–2.3) 0.2% (−0.3–0.7) 0.0085 Posterior cingulate cortex, left 2.1% (1.2–3.0) 0.9% (0.5–1.4) 0.01 CSF+ cerebrospinal fluidterm·key Aβterm < 192 ng/L, PETterm·key+ >0.87 SUVR, ROI region of interest, SUVR standardized uptake value ratio (using a composite reference region). Comparisons in ADNI between early Aβ accumulatorsterm·dom (CSF+/PETterm·key−) and non-accumulators (CSF−/PETterm·key−) subjects using general linear models with the Aβterm PETterm·key SUVR change/year in each of the 68 cortical Freesurfer ROI:s as the dependent variable and CSF/PETterm·key groups, sex, age, and time between PETterm·key scans as covariates. Data are given in mean values (95% CI of the mean) in order of significance and Aβterm accumulation is shown in % yearly SUVR change. Only the 15 significant regions after the Benjamini & Hochberg correction are shown. These regions were also significant when adjusting for changes in cortical thickness over 2 years (in each region) and diagnosis (MCI or healthy control) Early Aβterm accumulating regions in CSF converters Previous studies have found that non-demented CSF−/PETterm·key− individuals, that during follow-up show abnormal CSF Aβ42term·var levels, might represent the earliest preclinical ADterm·key phase 13 , 14 . We therefore examined CSF−/PETterm·key− subjects who, after 2 years, converted to CSF+/PETterm·key− (referred to as CSF converters). In the ADNI cohort there were 112 CSF−/PETterm·key− subjects with longitudinal CSF data that could be included in this analysis. At follow-up investigation, 11 of these 112 subjects had converted to abnormal CSF Aβ42term·var levels (CSF+/PETterm·key−) while 101 subjects remained stable with normal CSF Aβ42term·var levels (CSF−/PETterm·key−). Significant differences in Aβterm accumulation rate (Aβterm PETterm·key SUVR/year) between the two groups were seen in the left posterior cingulate and right medial orbitofrontal cortex ( p = 0.02–0.03). The group comparisons were still significant after adjustment of age, gender, and time between PETterm·key scans, but probably because of the limited statistical power ( n = 11) there were no significant differences after multiple comparison correction. Given the agreement with the previously identified regions (Fig. 1a, b and Table 2 ), we interpreted the identified regions as true and not a statistical type 1 error. For the CSF converters, the SUVR increased significantly with mean rates of +2.3–2.5% per year in the left posterior cingulate cortex and the right medial orbitofrontal cortex. No significant increase was seen in these regions among the non-converters and the corresponding mean rates were +0.3% (95% CI −0.24 to +0.90%) and +0.3% (95% CI −0.7 to +1.2%).

Aβterm accumulation and functional brain networks The regional distribution of the early accumulation of cortical Aβterm fibrils (Fig. 1a, b ) resembled the brain regions involved in the DMNterm·dom. This led us to examine early accumulation of Aβterm fibrils within the different functional brain networks. We applied the early accumulation regions from Fig. 1a to a functional connectivityterm·key network atlas 15 in order to determine the involvement of the different networks in early Aβterm accumulation. Figure 2 shows that early Aβterm accumulation predominantly overlapped with the DMNterm·dom (Jaccard coefficientterm·mth, JC, 0.14), but also with the frontoparietal networkterm·dom (JC 0.07). The networks that were the least involved in early Aβterm accumulation were the sensorimotor (JC 0.01) and visual networks (JC 0.01). Fig. 2 Distribution of early Aβterm accumulation among functional networks in ADNI. The bars show the distribution of the overlap between the significant cluster of early Aβterm accumulation (shown in red, see also Fig. 1a ) and the functional network ROIs (shown in blue above the bars). The sum of the seven bars is therefore 100%. The pattern of early stage Aβterm fibrils overlapped mostly with the DM followed by the FP network. The Jaccard coefficientterm·mth describes the similarity between the early Aβterm accumulation region and the functional network ROIs. It is calculated as the overlap between the early Aβterm region and the network ROI/(early Aβterm region + network ROI - overlap between early Aβterm region and the network ROI). DA dorsal attention, DM default mode, FP frontoparietal, FT frontotemporal, SM sensory motor, VA ventral attention, VI visual network Validation of our Aβterm staging by identifying late Aβterm regions We have used the CSF+/PETterm·key− subjects as a proxy for early stage Aβterm accumulators and compared them with CSF−/PETterm·key− subjects (non-accumulators) to identify early Aβterm regions. If our CSF/PETterm·key groups are valid for staging Aβterm phases, we should also be able to compare early Aβ accumulatorsterm·dom and CSF+/PETterm·key+ subjects (our proxy for late stage Aβterm accumulators) to identify known regions of late Aβterm accumulation. The results from the ROI-based comparisons are shown in Table 3 and Supplementary Movie 2 . The largest differences were seen in the precentral, postcentral, paracentral, pericalcarine, and lingual regions. This means that our proxy group for late Aβterm accumulation (CSF+/PETterm·key+) had a higher accumulation rate of the Aβterm PETterm·key signal over time compared with early Aβ accumulatorsterm·dom (CSF+/PETterm·key−) around the sensorimotor cortex and occipital lobe. Note that these significant, late Aβterm regions did not overlap with the early Aβterm regions (compare Tables 2 and 3 ). The corresponding voxelwise comparisons between early and late accumulators are shown in Fig. 3 and highlight similar late regions (greatest significant difference in the sensorimotor cortex and parts of the occipital lobe). Table 3 Annual Aβterm accumulation rates in the significant late Aβterm regions in ADNI Region CSF+/PETterm·key− CSF+/PETterm·key+ p -value Precentral, right 0.1% (−0.5–0.7) 2.1% (1.7–2.6) 0.000002 Postcentral, right 0.1% (−0.7–0.9) 2.3% (1.8–2.7) 0.000005 Pericalcarine, left 0.2% (−0.8–1.3) 2.8% (2.2–3.6) 0.000006 Postcentral, left 0.2% (−0.5–1.2) 2.4% (1.9–2.8) 0.000008 Precentral, left 0.3% (−0.3–1.0) 2.3% (1.9–2.9) 0.000024 Paracentral, left 0.6% (−0.2–1.4) 2.4% (1.9–2.8) 0.000057 Lingual, left 0.5% (−0.7–1.9) 2.8% (2.2–3.4) 0.00019 Lateral occipital, left 0.4% (−0.4–1.3) 2.3% (1.8–2.9) 0.00026 Pericalcarine, right 0.4% (−0.6–1.5) 2.9% (2.3–3.6) 0.0003 Paracentral, right 0.6% (−0.2–2.1) 2.2% (1.7–2.7) 0.00038 Fusiform, left 1.1% (0.3–2.0) 2.4% (2.0–2.8) 0.00049 Lingual, right 0.6% (−0.4–1.8) 2.8% (2.2–3.5) 0.0006 Cuneus, left −0.3% (−1.2–0.8)) 2.0% (1.4–2.8) 0.00062 Middle temporal, left 1.1% (0.3–2.0) 2.2% (1.9–2.6) 0.0007 Lateral occipital, right 0.6% (−0.1–1.4) 2.4% (1.9–3.1) 0.00088 Fusiform, right 0.9% (0.0–1.8) 2.4% (1.9–2.9) 0.0017 Inferior temporal, right 1.3% (0.5–2.2) 2.4% (1.9–2.8) 0.0022 Inferior temporal, left 1.5% (0.7–2.5) 2.6% (2.2–3.1) 0.003 Superior temporal sulcus, left 1.4% (0.6–2.3) 2.4% (1.9–2.8) 0.0047 Caudal middle frontal, left 1.3% (0.5–2.1) 2.5% (2.0–3.1) 0.0054 Superior parietal, left 1.0% (0.2–1.9) 1.9% (1.5–2.3) 0.0074 Superior frontal, left 1.2% (0.5–2.0) 1.9% (1.5–2.3) 0.013 CSF+ cerebrospinal fluidterm·key Aβ42 < 192 ng/L, PETterm·key+ >0.872 SUVR, ROI region of interest, SUVR standardized uptake value ratio (using a composite reference region). Comparisons in ADNI between early Aβ accumulatorsterm·dom (CSF+/PETterm·key−) and late Aβterm accumulators (CSF+/PETterm·key+) to identify late stage Aβterm accumulation regions. The analysis was performed using general linear models with the Aβterm PETterm·key SUVR change/year in each ROI as the dependent variable and CSF/PETterm·key groups, sex, age, and time between PETterm·key scans as covariates. Data are given in mean values (95% CI of the mean) in order of significance and Aβterm accumulation is shown in % yearly SUVR change. Only significant regions after the Benjamini & Hochberg correction are shown Fig. 3 Late Aβterm accumulation regions in ADNI. Voxelwise comparisons of annual SUVR change in early Aβ accumulatorsterm·dom (CSF+/PETterm·key−, n = 59) and late Aβterm accumulators (CSF+/PETterm·key+, n = 191). A significantly increased SUVR rate is seen in the sensorimotor cortex, occipital lobe, and dorsal temporal lobe in late compared with early Aβ accumulatorsterm·dom. Voxelwise two-sample t -tests were used and all comparisons were adjusted for age and gender. The significance threshold was set at p < 0.001. The red and yellow colors illustrate significant t values according to the scale on the left Atrophy and glucose metabolism in relation to Aβterm accumulation To test the hypothesis that atrophy, as a marker of neuronal and synaptic degeneration, starts after the initiation of Aβterm accumulation, we compared the longitudinal change in gray matter (GM) volumes between the CSF/PETterm·key groups using voxel-based morphometry (VBM) analysis (Fig. 4a, b and Supplementary Fig. 2A ). No clear atrophy pattern was seen in early Aβ accumulatorsterm·dom (CSF+/PETterm·key−) compared with non-accumulators (CSF−/PETterm·key−) (Fig. 4a ). In contrast, late Aβterm accumulators (CSF+/PETterm·key+) exhibited the expected longitudinal atrophy pattern of over time involving mainly temporoparietal regions when comparing them to non-accumulators (Fig. 4b ) and early Aβ accumulatorsterm·dom (Supplementary Fig. 2A ). Similar analyses were made with longitudinal FDG PETterm·key data to examine changes in glucose metabolism in relation to early Aβterm accumulation. There were no differences in FDG uptake over time between non-accumulators (CSF−/PETterm·key−) and early Aβ accumulatorsterm·dom (CSF+/PETterm·key−) (Fig. 4c ), while a cortical metabolic reduction in the temporal lobe was seen in late Aβterm accumulators (CSF+/PETterm·key+) compared with non-accumulators (CSF−/PETterm·key−) (Fig. 4d ) and early Aβ accumulatorsterm·dom (CSF+/PETterm·key−) (Supplementary Fig. 2B ). Fig. 4 Group comparisons of annual change in brain volume and glucose metabolism in ADNI. a Voxel-based morphometry (VBM) comparison of annual MRI change over 2 years in early Aβ accumulatorsterm·dom (CSF+/PETterm·key−, n = 59) compared with non-accumulators (CSF−/PETterm·key−, n = 218). The results are adjusted for age, gender, and intracranial volume. The contrast is reduction in cortical thickness in early compared with non-accumulators and shows no clear pattern of longitudinal atrophy in the early Aβ accumulatorsterm·dom (CSF+/PETterm·key−). b The same VBM analysis as in a , but with a comparison between non-accumulators and late Aβterm accumulators (CSF+/PETterm·key+, n = 191). The contrast is reduction in late compared with non-accumulators and shows a typical ADterm atrophy pattern in the non-demented late Aβterm accumulators (CSF+/PETterm·key+). c Voxelwise analysis of annual FDG PETterm·key change over 2 years in non-accumulators ( n = 153) compared with early Aβ accumulatorsterm·dom ( n = 41). No significant difference is seen. d The same FDG PETterm·key analysis as above, but with a comparison between non-accumulators and late Aβterm accumulators ( n = 124). The contrast is reduction in glucose metabolism in late compared with early Aβ accumulatorsterm·dom and shows a temporal and to a lesser extent parietal reduction in metabolism in the non-demented late Aβterm accumulators. Voxelwise two-sample t -tests were used and all results in a – d were adjusted for age and gender. The VBM analysis was also adjusted for total intracranial volume. Only voxels with a p < 0.001 are shown. The colors illustrate significant t values according to the scales Replication of the early Aβterm regions in the BioFINDER cohort To test the generalizability of our results we replicated the early Aβterm accumulation regions in the BioFINDER cohort. Only cross-sectional data was available from the BioFINDER study. Although longitudinal comparisons are preferred given our hypothesis, regional differences in Aβterm deposition should still be noticeable when comparing CSF+/PETterm·key− and CSF−/PETterm·key− subjects cross-sectionally. A strength of this cohort is that another Aβterm PETterm·key ligand ( 18 F -flutemetamol) as well as another immunoassay for CSF Aβterm (INNOTEST) were used, and the results would therefore be more generalizable if they were replicable in this cohort. The characteristics of BioFINDER is provided in Supplementary Table 2 . Similar early Aβterm accumulation regions were identified as in ADNI when comparing cross-sectional 18 F-flutemetamol SUVRs between CSF+/PETterm·key− and CSF−/PETterm·key− subjects using ROI-based analyses in BioFINDER (Supplementary Table 3 ). The most significant regions were found in the orbitofrontal cortex, the posterior cingulate cortex, and the precuneus (Supplementary Table 3 , which can be compared with ADNI data in Table 2 ). Further, the cross-sectional voxelwise comparisons between CSF+/PETterm·key− and CSF−/PETterm·key− subjects in BioFINDER showed significant changes predominantly in the orbitofrontal cortex and posterior cingulate similar to the longitudinal analyses in ADNI (Fig. 5 , compare with ADNI data in Fig. 1a, b ). Fig. 5 Replication of the early Aβterm regions in BioFINDER. Comparison of 18 F-flutemetamol SUVR between early accumulators (CSF+/PETterm·key−, n = 30) and non-accumulators (CSF−/PETterm·key−, n = 219) in BioFINDER to identify early Aβterm regions. The highest significance was seen around the posterior regions of the cingulate cortex and the orbitofrontal cortex similar to the results in ADNI (Fig. 1a, b ). Note that the BioFINDER analysis included cross-sectional data and fewer subjects compared to ADNI, which results in less statistical power. Voxelwise two-sample t -test was used. The significance threshold was set at p < 0.001 and the comparison was adjusted for age and gender. The red/yellow colors illustrate significant t values according to the scale on the left Early Aβterm accumulation and functional connectivityterm·key Resting-state functional MRI was available in the BioFINDER study and this cohort was therefore used for this analysis. To examine the association between early Aβterm accumulation and functional networks beyond the anatomical similarities (Fig. 2 ) we correlated levels of CSF Aβ42term·var with whole-brain connectivity (Fig. 6 ). The groups of interest were the early Aβ accumulatorsterm·dom (CSF+/PETterm·key−) and biomarker normal individuals with close to abnormal CSF Aβ42term·var (CSF- low /PETterm·key−). In the CSF+/PETterm·key− group we found a significant network component where reduced connectivity was associated with decreasing levels of CSF Aβ42term·var. The component predominantly consisted of intra-DMN links, but also links between the DMNterm·dom and the frontoparietal networkterm·dom (Fig. 6a ). The summed connectivity on the significant correlation component changed most drastically when CSF Aβ42term·var levels were closer to the cut-off (400–516 ng/L for the INNOTEST ELISA) and seemed to reach a floor effect as Aβ42 reached levels of around 350 ng/L. Figure 6c depicts the intra-DMN links, which dominate the significant hypoconnectivityterm·dom component correlating with lower CSF Aβ42term·var values in the early Aβ accumulatorsterm·dom. Furthermore, a significant network component involving the DMNterm·dom was associated with CSF Aβ42term·var in the CSF- low /PETterm·key− group, but with fewer constituent links (Fig. 6b ). Interestingly, the correlation was in the opposite direction showing an increased connectivity as CSF Aβ42term·var levels dropped from normal levels towards the abnormal cut-off. In summary, Fig. 6 indicates that when CSF Aβ42term·var levels drop within the near abnormal range they might be associated with an increased connectivity in connections involving the DMNterm·dom. Then, when CSF Aβ42term·var levels become abnormal and decrease further as Aβterm accumulates, they are instead clearly associated with a decreased connectivity within the DMNterm·dom and mainly between the DMNterm·dom and the frontoparietal networkterm·dom. The associations with hypoconnectivityterm·dom and hyperconnectivity were similar when adjusting for CSF P-tau and T-tau, indicating the independent effect of Aβterm on the connectivity (Supplementary Fig. 3 ). Fig. 6 Association between functional connectivityterm·key and early Aβterm accumulation in BioFINDER. a and b show CSF Aβ42term·var correlations with whole-brain resting-state fMRI connectivity in non-demented subjects with normal Aβterm PETterm·key and QC-passed fMRI data ( n = 103). Nodes were grouped into the 7 networks and 2 subcortical regions in the connectograms ( a and b ). The connectograms show that the significant network component associated with CSF Aβ42term·var was qualitatively similar for the CSF+/PETterm·key− ( a ) and CSF- low /PETterm·key− ( b ) groups and consisted mostly of intra- and inter-DM connections. Intra-DM dominated and the strongest inter-DM connection was with the frontoparietal networkterm·dom. The network component of the CSF+/PETterm·key− group exhibited reduced connectivity with decreasing CSF Aβ42term·var ( a ), r = 0.91, p < 0.001. By contrast, in the qualitatively similar network component of the CSF- low /PETterm·key− group, decreasing Aβ42 was instead associated with increased connectivity ( b ), r = −0.85, p < 0.001. Panel c shows the dominating intra DM-reduction in connectivity for the CSF+/PETterm·key− group ( a ). In summary, a show that Aβterm accumulation measured with CSF Aβ42term·var in early Aβ accumulatorsterm·dom is associated with hypoconnectivityterm·dom in intra- and inter-DM network links. b instead shows that in a population including subjects with even earlier indications of Aβterm accumulation (CSF- low /PETterm·key−) Aβterm accumulation is instead associated with hyperconnectivity in similar neuronal connections. Correlation coefficients ( r ) refer to Spearman correlation between summed connectivity and CSF Aβ42term·var levels. Age, gender, and APOE ε4 status was controlled for by partial correlation. Network components correlating with CSF Aβ42term·var were calculated using a method similar to the NBS algorithm (see “Methods for the BioFINDER study” for statistical details). Acronyms: BG basal ganglia, CSF- low normal CSF Aβ42term·var levels close to the abnormal cut-off (517–750 ng/L), DA dorsal attention, DM default mode, FP frontoparietal, FT frontotemporal, HI hippocampus, QC quality control, SM sensorimotor, VA ventral attention, VI visual network Discussion This study examined where Aβterm fibrils are more prone to start accumulating, by comparing the longitudinal outcome of non-demented subjects with early signs of Aβterm accumulation (CSF+/PETterm·key−) to those with no measurable signs of Aβterm accumulation (CSF−/PETterm·key−), in a large cohort of 473 subjects. The results showed significantly increased rates of Aβterm fibril accumulation predominantly in the precuneus, posterior cingulate cortex, and orbitofrontal cortex in this early Aβterm stage. When examining subjects with even earlier signs of Aβterm accumulation (CSF−/PETterm·key− subjects who converted to CSF+/PETterm·key− within 2 years), significantly increased Aβterm fibril accumulation rate was again seen in the medial orbitofrontal and posterior cingulate cortex, compared with stable CSF−/PETterm·key− subjects. These regions were replicated in an independent cohort ( n = 406). To our knowledge, this is the first study to identify a group of early stage Aβterm accumulators and detect increased Aβterm accumulation rates specifically in these regions using longitudinal data. Previous longitudinal studies have either failed to detect an early increase in regional Aβterm deposition 16 or identified a rather large early stage Aβterm region including the posterior temporal, inferior parietal, posterior cingulate, precuneus, and occipital areas 4 . Those previous results indicate that there might have been methodological issues in correctly identifying the preclinical ADterm·key cases who have just started to accumulate Aβterm.

There are a few previous studies where cross-sectional results have been used to approximate early stage Aβterm accumulation regions. In a subanalysis of a study aimed to identify different Aβterm PETterm·key cut-offs, voxelwise comparisons were made between a reference group with very low Aβterm PETterm·key SUVR ( n = 22) and several groups of subjects ( n = 22 in each group) with increasingly higher global SUVRs 17 . They found that the first significant SUVR differences between the controls and a group with sub-threshold SUVRs were located in the medial frontal cortex and the precuneus, similar to our early Aβterm regions. Another cross-sectional study used a different methodology to approximate the cortical spread of Aβterm in 67 healthy controls and 128 patients with MCI or ADterm dementia 18 . By assuming that earlier regions would show increased Aβterm PETterm·key signal in a greater number of subjects, they found an initial Aβterm region involving the areas in Fig. 1a , but also affecting middle and inferior temporal regions, and superior and middle frontal regions. In neuropathological studies, the results are mixed. One study reported that the presumably first Aβterm phase had Aβterm deposits throughout the neocortex 5 , while a prior publication by Braak & Braak 19 showed an early pattern of Aβterm deposits in the basal parts of the medial and anterior regions of the prefrontal cortex, and the basal anterior temporal lobe. The differences are probably partly explained by the different staining methods and their affinity to different Aβterm forms (discussed in the methodological aspects below). A clear advantage of the present study is that longitudinal Aβterm changes in the whole brain has been analyzed, instead of cross-sectional examinations in predetermined regions as in neuropathological studies.

The early Aβterm regions we identified were mainly located within the DMNterm·dom (Fig. 2 ). The DMNterm·dom has a core network located in the posterior cingulate cortex, precuneus, and medial prefrontal cortex 20 . It is almost always active, but increasingly so during daydreaming, activation of episodic memory and other internally directed activities and decreasingly during task-based activities and interaction with the external environment 21 , 22 . A certain overlap between the DMNterm·dom and the distribution of Aβterm fibrils in ADterm has also been noted in previous publications 23 – 26 . The cause of Aβterm aggregation within the DMNterm·dom is currently unknown. One explanation is that the deposition of Aβterm fibrils could be caused by the high neuronal activityterm·dom within the DMNterm·dom, which is supported by animal studies showing that Aβterm secretion and deposition is enhanced by neuronal activityterm·dom 27 – 29 . An alternative explanation is that Aβterm accumulation is caused by the high metabolic demand and metabolic stress in these neurons (high energy consumption and frequent fluctuations in activation and deactivation) and not the neuronal activation per se 30 . Although the core DMNterm·dom was the most prominent network that we found that had anatomical overlap with the early Aβterm regions, we want to point out that involvement of other networks was also evident in the early Aβterm accumulation, especially the frontoparietal networkterm·dom (Fig. 2 ). This indicates that early stage accumulation is not related to a unique feature of the DMNterm·dom, but in a more general sense to hubsterm·dom in the brain with high connectivity. This interpretation is supported by previous studies showing an association between Aβterm distribution and several networks and network hubsterm·dom 25 , 31 , 32 .

The association between functional networks and earliest stages of Aβterm accumulation was further examined using functional connectivityterm·key analyses in BioFINDER. When we correlated whole-brain functional connectivityterm·key and CSF Aβ42term·var levels in the early stage Aβterm accumulators (CSF+/PETterm·key−), we found a decreased connectivity in intra-DMN connections and between DMNterm·dom and the frontoparietal networkterm·dom as CSF Aβ42term·var decreased (Fig. 6a ). The disruption of these connections in BioFINDER is especially interesting given the distribution of early Aβterm fibrils in ADNI in the DMNterm·dom and frontoparietal networksterm·dom (Fig. 2 ). This correlation between the disruption of functional connectivityterm·key and Aβterm pathology has recently been described in ADterm 32 , 33 , but to our knowledge the present study is the first to show that this association is present already at the very earliest preclinical stages of ADterm when Aβterm fibrils are just starting to accumulate. In our second connectivity analysis (Fig. 6b ) we examined the correlation between functional connectivityterm·key and CSF Aβ42term·var in those with indications of very early Aβterm accumulation (close to abnormal CSF Aβ42term·var and normal Aβterm PETterm·key; CSF- low /PETterm·key−). The results indicate that when Aβ42 drops towards abnormal levels in the CSF the connectivity instead increases in similar DMN-related connections in which there was a positive correlation between decreased connectivity and decreasing levels of CSF Aβ42term·var within the abnormal Aβ42 range (compare connectograms in Fig. 4a, b ). Interpretations of this preliminary finding are speculative, but it might be that cortical hubsterm·dom like the medial frontal lobe and posterior cingulate exhibit increased activity before Aβterm fibril-containing senile plaques are formed in preclinical ADterm·key. This is supported by experimental cell and animal work suggesting that neural activity leads to increased Aβterm generation and secretion 27 , 34 . Later, the hypoconnectivityterm·dom might develop when Aβterm fibrils starts accumulating and causes neuronal dysfunction. Alternatively, early and soluble Aβterm oligomers might instead trigger the initial hyperactivity, which is supported by studies in transgenic ADterm models suggesting that neurons become hyperactive very early, independently of the deposition of Aβterm into plaques, and that the silencing of neurons emerges only later in the disease course 35 .

Although altered functional connectivityterm·key was found already in the early Aβ accumulatorsterm·dom (CSF+/PETterm·key−), our longitudinal analyses (Fig. 4a, c ) showed that no changes in glucose metabolism or atrophy were present at this stage. However, decreased temporoparietal glucose metabolism and atrophy was observed in the late Aβterm accumulators (CSF+/PETterm·key+; Fig. 4b, d and Supplementary Fig. 2A, B ). This finding can have practical implications for the enrollment in anti-Aβ clinical trials since anti-Aβ agents should be introduced after Aβterm accumulation has started but before neurodegenerationterm·dom is present. This should make CSF+/PETterm·key− subjects suitable for inclusion in clinical trials, especially if the drug is intended to target Aβterm accumulation over long time periods using ADterm biomarkers as outcomes of its efficacy in this preclinical disease stage.

The temporal sequence of Aβterm accumulation preceding neurodegenerationterm·dom confirms the most acknowledged hypothetical model on the development of ADterm 1 . It also strengthens the rationale for using CSF/PETterm·key groups as proxies for Aβterm stages in ADterm. To further validate the use of CSF/PETterm·key groups, we compared Aβterm accumulation in our proxy group for early Aβterm accumulation (CSF+/PETterm·key−) with the proxy group for late Aβterm accumulation (CSF+/PETterm·key+). As hypothesized, we identified known regions of late stage Aβterm accumulation, including the sensorimotor cortex and occipital lobe (Fig. 3 , Supplementary Movie 2 and Table 3 ) 3 , 19 . In a recent study, we also showed that CSF+/PETterm·key− subjects accumulated Aβterm at a similar rate as CSF+/PETterm·key+ subjects and about four times higher than CSF−/PETterm·key− subjects 12 . The high accumulation rate in CSF+/PETterm·key− subjects was not accompanied by cognitive decline, but we found a subtle decline in memory over 5 years in CSF+/PETterm·key+ subjects that further validates the use of CSF/PETterm·key groups for stratifying Aβterm stages. Nonetheless, we acknowledge that CSF+/PETterm·key− subjects are not all early Aβ accumulatorsterm·dom since this state also can be caused by CSF analytical factors, failed PETterm·key scans, and medical conditions other than ADterm 36 – 38 .

As part of our methodology we chose to primarily compare longitudinal changes between the CSF/PETterm·key groups instead of comparing cross-sectional values or just within-group changes to the identify the early Aβterm regions. This had the advantage of accounting for physiological differences in Aβterm burden, age-related non-specific Aβterm accumulation rates, and the fact that the individuals within each CSF/PETterm·key stage had reached slightly different time points in the disease progression. In addition, the early Aβ-accumulating brain regions remained significant when adjusting for confounding factors such as longitudinal changes in cortical thickness, APOE genotype and clinical status. These adjustments show that the results were not confounded by differences in atrophy between the groups or differences between healthy controls and MCI subjects (discussed further in the Methods section). It should be noted that the early Aβterm regions are derived from group analyses, which show where Aβterm is prone to start accumulating but they do not exclude the possibility that the accumulation might start in other regions in some individuals.

A limitation of our early Aβterm regions is that they have been identified using PETterm·key ligands that bind to primarily dense-core Aβterm aggregates and to a lesser extent or not at all to diffuse plaques 39 , 40 . We can therefore not pinpoint the exact Aβterm forms within the early Aβterm regions, neither can we say if Aβterm aggregates not detected by PETterm·key are present in other areas of the brain at this early stage. Future PETterm·key ligands that binds to different forms, e.g., Aβterm protofibrils, might reveal a different early Aβterm pattern 41 . The binding affinity of current PETterm·key ligands could explain some of the differences with neuropathological studies that either use silver technique 19 , which stain not only Aβterm but also tau, or anti-Aβ antibodies 5 that bind to specific Aβterm epitopes and stain different Aβterm forms. Nonetheless, a strength of the methodology is that we could detect similar early Aβterm regions in two different cohorts using two different types of PETterm·key ligands; 18 F-florbetapir (a stilbene compound) and 18 F-flutemetamol (an 18 F-labeled analog of 11 C-Pittsburgh Compound B (PiB) derived from thioflavin T) 40 . The cut-offs for the two PETterm·key ligands were established separately in each cohort, and the data were not pooled in the analyses, nor were the groups compared to each other. We therefore chose to not transform SUVR into a standardized scale such as centiloid units 42 .

To conclude, we believe that the identified early Aβterm regions can have several important implications. These brain regions give a pathophysiological insight into where Aβterm fibril accumulation is prone to start in ADterm and its relationship to functional networks and their connectivity. Our data indicated that Aβterm starts accumulating before overt metabolic changes or atrophy, and that hypoconnectivityterm·dom within the early Aβ-accumulating regions has already occurred when Aβterm fibrils just starts to accumulate. The most prominent early Aβ-accumulating regions were the precuneus, posterior cingulate cortex, and the medial orbitofrontal cortex, possibly with an overlap to the anterior cingulate. Based on our results, future studies should be able to examine early unique pathophysiological events and triggering mechanisms of Aβterm accumulation by comparing molecular and physiological properties of the early-accumulating regions to later-accumulating regions. These early-accumulating Aβterm regions should also be advantageous to use in an Aβterm PETterm·key composite ROI to better assess early fibrillar Aβterm deposition (available at http://biofinder.se ), which can have practical implications for early ADterm diagnostics, Aβterm staging and enrollment in clinical trials that target Aβterm accumulation.

Methods Study data Longitudinal data from the ADNI were used for all the statistical analyses in the study, except for replicating the finding of early Aβterm accumulating regions and for the functional connectivityterm·key analyses for which data from the Swedish BioFINDER study were used. The methodology described in the next sections refers only to analyses performed in the ADNI cohort. Methodology referring to analyses in the BioFINDER cohort is described separately under Methods for the BioFINDER study.

Participants Only non-demented individuals were included in this study (characterized as either cognitively normal or diagnosed with mild cognitive impairment, MCI). Demented subjects were excluded since the study aim was to identify early ADterm pathology. We chose to include MCI subjects in addition to healthy controls since the cognitive impairment did not necessarily have to be caused by an underlying ADterm process and because about a third of MCI subjects have shown to be incorrectly diagnosed with cognitive impairment 43 – 45 . We also attempted to include a range of different pre-dementia ADterm phases for a better staging of late Aβterm accumulators, early Aβ accumulatorsterm·dom and non-accumulators. To account for this inclusion the models were adjusted for clinical status (MCI/healthy control) in subanalyses. The specific inclusion/exclusion criteria for the ADNI cohort can be found at http://www.adni-info.org . Briefly, all subjects were enrolled from the ADNI-2 study, were between the ages of 55 and 90 years, were fluent in Spanish or English, had completed at least six years of education, had a Mini-Mental State Examination score (MMSE) of ≥24 46 , and were free of any significant neurologic disease other than ADterm. Subjects classified as cognitively normal ( n = 176) had a Clinical Dementia Rating scale (CDR) score of 0 47 . MCI ( n = 297) was defined as having preserved activities of daily living, absence of dementia, and an objective cognitive impairment as shown on the delayed recall test of the Wechsler Memory Scale - Logical Memory II as well as a CDR score of 0.5. Only subjects with a complete set of baseline and follow-up Aβterm PETterm·key scans and baseline CSF Aβ42term·var data were included. Our study baseline was defined as the first visit where both CSF and PETterm·key data were available. Study subjects gave written informed consent and the study was approved by each participating site’s Institutional Review Board.

Image acquisition For MRI, 3 Tesla scanners were used. High-resolution 3D T1-weighted images were acquired for volumetric measures, anatomical segmentation, and template normalization using an MPRAGE sequence (for details, see ref. 48 ). Fibrillar Aβterm pathology was measured using the PETterm·key ligand 18 F-florbetapir. Image data were acquired 50–70 minutes post-injection (see http://adni.loni.usc.edu/wp-content/uploads/2010/05/ADNI2_PETterm·key_Tech_Manual_0142011.pdf ). Glucose metabolism was measured employing FDG PETterm·key with images acquired 30–60 min post-injection. Florbetapir PETterm·key, FDG PETterm·key, and MR images from baseline and the 2-year follow-up visit were downloaded from the LONI database ( http://ida.loni.usc.edu ) and further processed locally. Longitudinal 18 F-florbetapir was available in all subjects, longitudinal MRI in 468 subjects (218 CSF−/PETterm·key−, 59 CSF+/PETterm·key−, and 191 CSF+/PETterm·key+), and longitudinal FDG PETterm·key in 318 subjects (153 CSF−/PETterm·key−, 41 CSF+/PETterm·key−, and 124 CSF+/PETterm·key+).

Image processing The motion corrected PETterm·key data was time-averaged and rigidly aligned with the anatomical MRI scan. SUVR were then calculated by normalizing to averages of appropriate reference regions. For florbetapir, the reference region consisted of the whole cerebellum, the pons/brainstem region, and eroded cortical white matter as this composite region has produced more reliable results in longitudinal analyses 49 . For FDG, a composite region comprising vermis/cerebellum superior to the horizontal fissure and pons were used as reference region 50 . Freesurfer version 5.3 ( http://surfer.nmr.mgh.harvard.edu ; Desikan-Killiany Atlas) segmentations of the structural MR images were applied to extract SUVRs from 68 predefined ROIs. Partial volume error (PVE) correction was performed for florbetapir SUVRs with the geometric transfer matrix (GTM) method 51 using Freesurfer segmented regions 52 (all florbetapir results shown in the tables and figures are from PVE corrected data).

The MRI volumes were further normalized to MNI152 standard space 53 using a nonlinear diffeomorphic transformation 54 enabling motion and PVE voxelwise 55 corrected SUVRs to be transformed to MNI standard space for voxelwise analyses. VBM as implemented in Statistical Parametric Mapping (SPM) version 12 (Wellcome Department of Cognitive Neurology, London, UK; http://www.fil.ion.ucl.ac.uk/spm ) was used to evaluate GM intensity as a measure of atrophy. All individual T1-weighted MR images were segmented into tissue classes using SPM’s unified segmentation algorithm. The GM segmentations were subsequently warped into a common MNI152 standard space using a cohort-specific template and the SPM Diffeomorphic Anatomical Registration Through Exponentiated Lie Algebra (DARTEL) toolbox and Jacobian scaled to estimate GM intensity. The resulting maps were smoothed with 8 mm full-width at half maximum (FWHM) Gaussian kernel. Intracranial volume (ICV) was calculated by summating the volumes of GM, white matter, and CSF based on the SPM tissue segmentations. Using the corrected SUVRs in standardized space, we also proceeded to calculate summed values within the functional MRI (fMRI) resting-state networks enumerated in the low resolution atlases with seven networks calculated by Yeo et al. (see details below) 15 .

Florbetapir and FDG PETterm·key images were co-registered to their respective MRI and subsequently warped into a common MNI152 space using the transformation information derived from warping the MRI scans. To assess changes over time in GM intensity (MRI), amyloid accumulation (florbetapir PETterm·key), and glucose metabolism (FDG PETterm·key) between baseline and follow-up examinations, parametric change maps were created. For longitudinal VBM, a midpoint average MRI was created for each subject based on the baseline and follow-up images using SPM12, resulting in Jacobian determinant maps showing local volumetric change between baseline and follow-up for each subject. Midpoint average images were GM-segmented and subsequently used to create a DARTEL template that was affine-transformed to the common MNI152 space. Midpoint average GM segmentations were multiplied by the Jacobian maps to create individual GM change maps, which were warped to the MNI-transformed DARTEL template and smoothed by an 8 mm FWHM isotropic Gaussian kernel to create longitudinal change maps. A GM mask was created using the same approach as for the cross-sectional data (see above). The florbetapir and FDG change maps were created using the warped images and FSL ( http://fsl.fmrib.ox.ac.uk ) according to the equation SUV R change = (PETterm·key imag e followup - PETimag e baseline )∕years between scans CSF biomarkers CSF Aβ42term·var, total tau (T-tau), and tau phosphorylated at Thr181 (P-tau) were measured using the multiplex xMAP Luminex platform (Luminex Corp, Austin, TX, USA) with the INNOBIA AlzBio3 kit (Innogenetics, Ghent, Belgium) 56 , 57 . Data was combined from the available online files “UPENNBIOMK8.csv”, “UPENNBIOMK7.csv, “UPENNBIOMK6_07_02_13.csv”, and “UPENNBIOMK5_10_31_13.csv”. Only cross-sectional CSF data from baseline were used in the analyses ( n = 473), except for the analysis of “CSF converters” where longitudinal 2-year data was used ( n = 112). In the case of one subject with stable Aβ42 levels, only 3-year follow-up data was available. The mean difference between baseline CSF sampling and the florbetapir PETterm·key scan was 9.6 days (ranging from 0 to 110 days).

Group classifications All subjects were categorized into different groups according to their CSF Aβ42term·var and Aβterm PETterm·key status. The following groups were derived from the baseline results: (1) normal CSF Aβ42term·var and Aβterm PETterm·key (CSF−/PETterm·key−) referred to as non-accumulators ( n = 218), (2) abnormal CSF Aβ42term·var and normal Aβterm PETterm·key (CSF+/PETterm·key−) referred to as early Aβ accumulatorsterm·dom ( n = 59), and (3) abnormal CSF Aβ42term·var and Aβterm PETterm·key (CSF+/PETterm·key+) referred to as late Aβterm accumulators ( n = 191). Those with normal CSF Aβ42term·var and abnormal Aβterm PETterm·key were not used in any analysis ( n = 5). Abnormal CSF Aβ42term·var values were defined using the previously established cut-off of <192 ng/L 57 – 59 . The cut-off for abnormal Aβterm PETterm·key SUVR was determined using a mixture modeling analysis since no previous cut-off had been established for PVE corrected florbetapir data in ADNI (using the GTM method). Mixture modeling statistics provide an unbiased threshold for abnormality (i.e., the diagnosis or status is not used in analysis) and it is a well-established method for defining Aβterm PETterm·key cut-offs 12 , 17 , 60 , 61 . To achieve a robust threshold we derived the cut-off from a larger ADNI sample ( n = 663, all non-demented subjects with CSF and Aβterm PETterm·key data). This resulted in a cut-off of >0.872 SUVR for an abnormal Aβterm PETterm·key using PVE correction and the composite reference region.

Methods for the BioFINDER study The Swedish BioFINDER study was used for replicating the main results from ADNI and for the functional connectivityterm·key analyses. BioFINDER is a prospective study that focuses on identifying key mechanisms and improvement of diagnostics in ADterm and other neurodegenerative disorders. For details about study design, methods, and specific inclusion/exclusion criteria, see http://biofinder.se . The study was approved by the ethical review board in Lund, Sweden, and all participants gave their written informed consent. All non-demented individuals with CSF and Aβterm PETterm·key data were selected for this study. This resulted in a cohort consisting of cognitively healthy elderly subjects ( n = 138) and consecutively recruited patients who had been referred to memory clinics due to cognitive complaints ( n = 268). The PETterm·key ligand 18 F-flutemetamol was used for Aβterm PETterm·key, and images were acquired 90–110 min post-injection. The PETterm·key scanning procedures have been described previously 60 . CSF Aβ42term·var and P-tau were analyzed with INNOTEST ELISAs (Fujirebio Europe, Ghent, Belgium) and T-tau with EUROIMMUN ELISAs (EUROIMMUN AG, Lübeck, Germany) as previously described 60 , 62 . Mixture modeling was performed in the sample to determine the cut-offs for abnormal CSF Aβ42term·var (<517 ng/L) and abnormal Aβterm PETterm·key (>0.759 SUVR). Only baseline data was available for analysis in the BioFINDER cohort.

Imaging was performed on a 3 Tesla Siemens Tim Trio scanner (Siemens Medical Solutions, Erlangen, Germany). The high-resolution 3D T1-weighted volume used for segmentation and normalization was acquired using an MPRAGE sequence (in-plane resolution = 1 × 1 mm 2 , slice thickness = 1.2 mm, TR = 1950 ms, TE = 3.37 ms, flip-angle = 9°). Spontaneous BOLD oscillations in the absence of external stimuli were imaged with a gradient-echo planar sequence (eyes closed, in-plane resolution = 3 × 3 mm 2 , slice thickness = 3mm, TR = 2000 ms, TE = 30 ms, flip-angle = 90°, 180 dynamic scans, 6 min).

Resting-state data preprocessing was performed with a pipeline composed of FSL 63 , AFNI 64 , and ANTS 65 . Anatomical processing involved skull stripping, segmentation of white matter (WM)/GM/CSF and normalization to MNI152-space 53 . Dropping the first five frames in anticipation of steady state, functional data was bulk motion, and slice timing corrected, furthermore nuisance regressed using the WM/CSF average signal, 6 components of physiological noise 66 , 24 motion parameters 67 , and linear/quadratic trends. Finally, the functional data was transformed to MNI space. Frames causing outliers in total frame-to-frame signal variation were censored, on average constituting 4% of the fMRI series 68 . The signal was band-pass filtered to 0.01–0.1 Hz, further discriminating against scanner drift and physiological noise. No spatial smoothing was applied.

Subjects with a mean/maximum frame-wise displacement 29 , 69 exceeding 0.6/3.0 mm were excluded. As an extra precautionary step, the voxel-to-voxel BOLD-signal correlations across the whole brain (including GM, WM, and CSF) was calculated and summed. Outliers in this measure likely originate in a motion-induced global signal confound capable of eluding conventional motion estimation 70 and were removed.

The processed fMRI data was resampled using trilinear interpolation to 6 × 6 × 6 mm 3 resolution and masked with GM derived from a cortical resting-state network atlas 71 and Harvard-Oxford subcortical atlas 72 . Fisher-z transformed Pearson correlation between the resulting 5071 GM voxel time series then yielded a measure of functional connectivityterm·key (FC), corresponding to a weighted graph with nodes (voxels) and links (voxel BOLD time series correlations).

Network components correlating with CSF Aβ42term·var were calculated using a method similar to the NBS algorithm 73 . We calculated the largest network component C (defined as a connected set of links), for which r ij > r 0 , where r ij is the Spearman correlation over all subjects between CSF Aβ42term·var and z ij for the link between voxel i and j (Fisher z-transformed voxel BOLD time-series correlation), and r 0 controls the component size and significance level of constituent links. We chose r 0 corresponding to approximately p = 0.001 given the number of subjects in the calculation. Component size was then defined as the sum of all r i j ϵ C multiplied by the Spearman correlation of the sum of all z -values in the component and CSF levels. This last step allows for reasonable and natural variations on the extracted component. The component size was compared to a permutation-generated null distribution of sizes, thus controlling for the family-wise error rate in the weak sense at α = 0.05 The result of the algorithm is a network component on which sum of z -scores correlates significantly higher than for randomized sets of subject FC-CSF pairs. Age, gender and APOE ε4 status was controlled for by partial correlation.

In order to simplify the analysis of network components, we grouped nodes using a resting-state network atlas 71 containing: default mode, dorsal and ventral attention, sensory motor, visual, fronto-parietal, and fronto-temporal (medial temporal lobe/orbitofrontal cortex). To this set of labels we added two anatomically defined subcortical structures from the Harvard-Oxford atlas 72 : the Basal Ganglia (BG: thalamus, caudate, putamen and pallidum) and hippocampus/amygdala (HI). Note that the permutation-based approach generates p values for the network component as a whole, but since these are too large and complex to visualize, a network-based break up is necessary.

Only Aβterm PETterm·key negative subjects were used in the connectivity analysis. In addition to the previously described group of early Aβ accumulatorsterm·dom (CSF+/PETterm·key−, n = 23 after fMRI quality control) we also defined a group of biomarker negative subjects with indications of very early Aβterm accumulation ( n = 80). Those with low levels of CSF Aβ42term·var, but still within the normal range, have a high risk of becoming abnormal within the next couple of years 13 , which suggests that sub-threshold CSF Aβ42term·var levels indicate very early Aβterm accumulation. This group was characterized as Aβterm PETterm·key negative with CSF Aβ42term·var between 517–750 μg/mL (CSF− low /PETterm·key−).

Statistical analysis Group comparisons of baseline characteristics (Table 1 and Supplementary Table 2 ) was performed with Mann–Whitney U statistics if significant after the Kruskal–Wallis test. For ROI analyses, full factorial general linear models were used to analyze group differences and correlations of the 68 available cortical Freesurfer ROIs from the Desikan-Killiany Atlas. The dependent variable was Aβterm accumulation rate ([SUVR at follow-up – SUVR at baseline]/years between baseline and follow-up) in ADNI and baseline Aβterm PETterm·key SUVR in BioFINDER. The variance of the SUVRs were similar in each group. All results were adjusted for gender and age and, in the longitudinal analyses, time between Aβterm PETterm·key examinations. We also performed additional analyses adjusting for changes in cortical thickness over 2 years in each ROI and clinical status (healthy control/MCI) in addition to the above covariates. Longitudinal changes within a specific group of subjects were analyzed with the Paired T-test. Because of the limited number of subjects in the subanalysis of “CSF converters” ( n = 11), we primarily used non-parametric statistics for this subgroup (the Mann–Whitney U and Wilcoxon signed rank test). To control for multiple testing in the ROI-based analyses we used the Benjamini–Hochberg procedure with a conservative false discovery rate (FDR) of 0.05 (not to be confused with a p value of 0.05) 74 . Original p values are presented for the ROI-based analyses but are only reported as significant if they are significant after the FDR correction. The effect size of the significant results when comparing the CSF/PETterm·key groups are given in % annual increase of SUVR (yearly SUVR change divided by SUVR at baseline) for an easier comparison between different PETterm·key ligands and brain regions (and when comparing the results to other studies that use different reference regions). However, statistical significances were determined using actual SUVR or SUVR/year data. SPM12 was used for all voxelwise analyses. We employed voxelwise two-sample t -test and multiple regressions using age, gender, and ICV (for analyses involving VBM data) as covariates. All images had been transformed into common MNI space by using transformation measures from warping the co-registered MRI scans to the 2 mm MNI152 MRI template and smoothed with an 8 mm FWHM kernel. The resulting t-maps were thresholded to present results with a p < 0.001 height, uncorrected for family-wise error, and a k > 100 voxels cluster extent. For the resting-state correlation analysis, a component (consisting a connected set of links correlating with CSF biomarker levels above a p = 0.001 threshold) was assigned a p value as a whole based on the component size relative a permutation-generated null distribution, thus correcting for family-wise cerror in the weak sense (see “Methods for the BioFINDER study” for details). Finite mixture models for establishing biomarker cut-offs were performed with the package “mixtools” in R version 3.2.2 (R Foundation for Statistical Computing, Vienna, Austria, 2013). FDR correction of multiple comparisons was performed using the Benjamini–Hochberg formula in an Excel sheet (Microsoft Excel for Mac, 2011, version 14.4). All other statistical analyses were performed with SPSS for Mac, version 22.0 (SPSS Inc., Chicago, IL).

Data availability MRI and PETterm·key images were downloaded online at https://ida.loni.usc.edu and further processed locally (see Image Processing above). All other ADNI data were also downloaded from the same site. BioFINDER data and processed ADNI data are not publicly available for download, but might be retrieved from the principal investigator Oskar HanssonpersName. A PET-data-derived ROI of the early-accumulating Aβterm regions (Fig. 1a ) is available in MNI152 space and Nifti-format at http://biofinder.se .

Electronic supplementary material Supplementary information Description of Additional Supplementary Files Supplementary Movie 1 Supplementary Movie 2 Supplementary Movie 3